





'''simple docstring'''






import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class 		lowerCamelCase	(   _A							):



	def __init__( self		,   a_		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = False		,   a_ = False		,   a_ = None		,   a_ = None		,   **a_		,   ):
			super().__init__(
			    a_		,   split=a_		,   features=a_		,   cache_dir=a_		,   keep_in_memory=a_		,   streaming=a_		,   num_proc=a_		,   **a_		,   )
			lowerCAmelCase  :     Tuple       						= field
			lowerCAmelCase  :     List[Any]       						= path_or_paths if isinstance(a_		,   a_						) else {self.split: path_or_paths}
			lowerCAmelCase  :     Tuple       						= Json(
			    cache_dir=a_		,   data_files=a_		,   features=a_		,   field=a_		,   **a_		,   )





	def 	_lowerCamelCase     ( self						):
			# Build iterable dataset
			if self.streaming:
					lowerCAmelCase  :     Dict       						= self.builder.as_streaming_dataset(split=self.split						)
			# Build regular (map-style) dataset
			else:
					lowerCAmelCase  :     List[Any]       						= None
					lowerCAmelCase  :     Dict       						= None
					lowerCAmelCase  :     List[str]       						= None
					lowerCAmelCase  :     Union[str, Any]       						= None

					self.builder.download_and_prepare(
					    download_config=a_		,   download_mode=a_		,   verification_mode=a_		,   base_path=a_		,   num_proc=self.num_proc		,   )
					lowerCAmelCase  :     str       						= self.builder.as_dataset(
					    split=self.split		,   verification_mode=a_		,   in_memory=self.keep_in_memory						)
			return dataset






class 		lowerCamelCase	:



	def __init__( self		,   a_		,   a_		,   a_ = None		,   a_ = None		,   **a_		,   ):
			if num_proc is not None and num_proc <= 0:
					raise ValueError(F'''num_proc {num_proc} must be an integer > 0.'''						)

			lowerCAmelCase  :     str       						= dataset
			lowerCAmelCase  :     Union[str, Any]       						= path_or_buf
			lowerCAmelCase  :     Any       						= batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
			lowerCAmelCase  :     Optional[Any]       						= num_proc
			lowerCAmelCase  :     Dict       						= "utf-8"
			lowerCAmelCase  :     Union[str, Any]       						= to_json_kwargs



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     List[str]       						= self.to_json_kwargs.pop("path_or_buf"		,   a_						)
			lowerCAmelCase  :     Dict       						= self.to_json_kwargs.pop("orient"		,   "records"						)
			lowerCAmelCase  :     Dict       						= self.to_json_kwargs.pop("lines"		,   True if orient == "records" else False						)
			lowerCAmelCase  :     List[str]       						= self.to_json_kwargs.pop("index"		,   False if orient in ["split", "table"] else True						)
			lowerCAmelCase  :     List[Any]       						= self.to_json_kwargs.pop("compression"		,   a_						)

			if compression not in [None, "infer", "gzip", "bz2", "xz"]:
					raise NotImplementedError(F'''`datasets` currently does not support {compression} compression'''						)

			if isinstance(self.path_or_buf		,   (str, bytes, os.PathLike)						):
					with fsspec.open(self.path_or_buf		,   "wb"		,   compression=a_						) as buffer:
							lowerCAmelCase  :     List[Any]       						= self._write(file_obj=a_		,   orient=a_		,   lines=a_		,   index=a_		,   **self.to_json_kwargs						)
			else:
					if compression:
							raise NotImplementedError(
							    F'''The compression parameter is not supported when writing to a buffer, but compression={compression}'''
							    " was passed. Please provide a local path instead."						)
					lowerCAmelCase  :     Dict       						= self._write(
					    file_obj=self.path_or_buf		,   orient=a_		,   lines=a_		,   index=a_		,   **self.to_json_kwargs						)
			return written



	def 	_lowerCamelCase     ( self		,   a_						):
			lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     Optional[int]       						= args

			lowerCAmelCase  :     Any       						= query_table(
			    table=self.dataset.data		,   key=slice(a_		,   offset + self.batch_size						)		,   indices=self.dataset._indices		,   )
			lowerCAmelCase  :     Optional[int]       						= batch.to_pandas().to_json(
			    path_or_buf=a_		,   orient=a_		,   lines=a_		,   index=a_		,   **a_						)
			if not json_str.endswith("\n"						):
					json_str += "\n"
			return json_str.encode(self.encoding						)





	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_		,   a_		,   **a_		,   ):
			lowerCAmelCase  :     int       						= 0

			if self.num_proc is None or self.num_proc == 1:
					for offset in logging.tqdm(
					    range(0		,   len(self.dataset						)		,   self.batch_size						)		,   unit="ba"		,   disable=not logging.is_progress_bar_enabled()		,   desc="Creating json from Arrow format"		,   ):
							lowerCAmelCase  :     Union[str, Any]       						= self._batch_json((offset, orient, lines, index, to_json_kwargs)						)
							written += file_obj.write(a_						)
			else:
					lowerCAmelCase						,		lowerCAmelCase  :     str       						= len(self.dataset						), self.batch_size
					with multiprocessing.Pool(self.num_proc						) as pool:
							for json_str in logging.tqdm(
							    pool.imap(
							        self._batch_json		,   [(offset, orient, lines, index, to_json_kwargs) for offset in range(0		,   a_		,   a_						)]		,   )		,   total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size		,   unit="ba"		,   disable=not logging.is_progress_bar_enabled()		,   desc="Creating json from Arrow format"		,   ):
									written += file_obj.write(a_						)

			return written










'''simple docstring'''






from __future__ import annotations

lowerCAmelCase									=   []






def   __A    (				a_    :	list[list[int]]   ,a_    :	int   ,a_    :	int						):
		for i in range(len(a_						)						):
				if board[row][i] == 1:
						return False
		for i in range(len(a_						)						):
				if board[i][column] == 1:
						return False
		for i, j in zip(range(a_   ,-1   ,-1						)   ,range(a_   ,-1   ,-1						)						):
				if board[i][j] == 1:
						return False
		for i, j in zip(range(a_   ,-1   ,-1						)   ,range(a_   ,len(a_						)						)						):
				if board[i][j] == 1:
						return False
		return True






def   __A    (				a_    :	list[list[int]]   ,a_    :	int						):
		if row >= len(a_						):
				solution.append(a_						)
				printboard(a_						)
				print()
				return True
		for i in range(len(a_						)						):
				if is_safe(a_   ,a_   ,a_						):
						lowerCAmelCase  :     Dict       						= 1
						solve(a_   ,row + 1						)
						lowerCAmelCase  :     Optional[int]       						= 0
		return False






def   __A    (				a_    :	list[list[int]]						):
		for i in range(len(a_						)						):
				for j in range(len(a_						)						):
						if board[i][j] == 1:
								print("Q"   ,end=" "						)
						else:
								print("."   ,end=" "						)
				print()


# n=int(input("The no. of queens"))
lowerCAmelCase									=   8
lowerCAmelCase									=   [[0 for i in range(n)] for j in range(n)]
solve(board, 0)

print("""The total no. of solutions are :""", len(solution))



